A new energy vehicle battery management system and method

Abstract

The application relates to the technical field of electric vehicle battery management, and particularly discloses a new energy automobile battery management system and method, which is provided with a battery pack state analysis module, a single battery equalization state analysis module, a battery state output module, a battery management error item analysis module and a battery management updating module, solves the problems that the traditional new energy automobile battery pack management environment and working condition adaptation capability are insufficient, battery pack health state evaluation is not accurate enough, and the single battery thermal management system is not optimized enough and the equalization effect is limited, and helps to accurately output the state of the battery according to different battery pack states and single battery equalization conditions, determine the battery management grade and management scheme after considering the error caused by environmental factors, reduce the loss of the battery in the use process, optimize the new energy automobile battery performance, and improve the system reliability and safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle battery management, in particular to a new energy vehicle battery management system and method. BACKGROUND

[0002] With the development of global economy, the dependence of traditional fuel vehicles on oil resources leads to energy supply tension. Under this background, new energy vehicles as a sustainable transportation solution have attracted widespread attention. In recent years, battery technology has made great progress. Lithium-ion batteries have high energy density, low self-discharge rate and long cycle life, and have become the mainstream energy storage device of new energy vehicles. However, battery technology still faces many challenges. On the one hand, the improvement of battery performance requires more precise management. On the other hand, in order to meet the demand of long cruising range and high power output of new energy vehicles, the battery pack is often composed of multiple single batteries in series and parallel connection. There are inevitable performance differences between single batteries, which will lead to the decline of the overall performance of the battery pack and shorten the service life of the battery pack if not managed. Under the concept of intelligent networked vehicles, vehicles need to have higher automation and more reliable safety performance. Through the battery management system, the occurrence of accidents can be effectively prevented, and the overall safety of new energy vehicles can be improved.

[0003] Nowadays, there are still some deficiencies in the research of new energy vehicle battery management, which are embodied in the insufficient adaptability of traditional new energy vehicle battery pack management environment and working condition, and the inaccurate evaluation of battery pack health status. Moreover, the single battery thermal management system is not optimized enough, and the balancing effect is limited. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a new energy vehicle battery management system and method, which can effectively solve the problems involved in the background technology.

[0005] To achieve the above object, the present application is realized by the following technical solutions: the present application provides a new energy automobile battery management system in the first aspect, including battery pack state analysis module, single body equalization state analysis module, battery state output module, battery management error item analysis module and battery management update module, wherein: the battery pack state analysis module is used for analyzing the new energy automobile battery pack state, and the new energy automobile battery pack state factor is obtained based on the new energy automobile battery pack state analysis result;The single body equalization state analysis module is used for analyzing the new energy automobile single body battery equalization state, and the new energy automobile single body battery equalization characteristic is obtained;The battery state output module is used for combining the new energy automobile battery pack state factor, the new energy automobile single body battery equalization characteristic, and using the neural network model, and outputting the new energy automobile battery state;The battery management error item analysis module is used for obtaining the new energy automobile battery use environment data, and the new energy automobile battery management error item is obtained based on the obtained new energy automobile battery use environment data;The battery management update module is used for obtaining the new energy automobile battery management level and management scheme based on the new energy automobile battery state and the new energy automobile battery management error item.

[0006] As a further scheme, the new energy automobile battery pack state is analyzed, and the specific analysis process is as follows: the capacity loss of the battery pack under different charge-discharge rates is considered, the rate correction coefficient is introduced, and the improved ampere-hour integral formula is obtained:

[0007]

[0008] In the formula, SOC is the state of charge of the battery pack, SOC0 is the initial state of charge of the battery pack, C n is the rated capacity of the battery pack, I b (t) is the battery pack charge-discharge current at time t, K r is the rate correction coefficient stored in the database;

[0009] The health condition of the battery pack is calculated:

[0010]

[0011] In the formula, SOH is the health condition of the battery pack, C a is the current actual available capacity of the battery pack, and A is the calendar aging factor stored in the database;

[0012] The relationship between the battery pack resistance growth and temperature is described by combining the Arrhenius equation, and the battery pack resistance growth model is constructed:

[0013]

[0014] In the formula, R i is the battery pack resistance, Ri0 is the initial internal resistance of the battery pack, β and γ are the fitting parameters stored in the database, E a is the activation energy, R is the gas constant, T is the absolute temperature of the battery pack, E is the cumulative charge and discharge energy of the battery pack, and e is the natural constant.

[0015] As a further scheme, based on the analysis result of the state of the battery pack of the new energy vehicle, the state factor of the battery pack of the new energy vehicle is obtained, and the specific analysis process is: based on the state of charge of the battery pack, the health status of the battery pack and the internal resistance of the battery pack, the state factor of the battery pack of the new energy vehicle is obtained by comprehensive analysis, and the state factor of the battery pack of the new energy vehicle is used as the analysis basis for outputting the state of the battery of the new energy vehicle.

[0016]

[0017] In the formula, Dcz is the state factor of the battery pack of the new energy vehicle.

[0018] As a further scheme, the equalization state of the single battery of the new energy vehicle is analyzed, and the equalization characteristics of the single battery of the new energy vehicle are obtained, and the specific analysis process is: the temperature of each single battery of the new energy vehicle is obtained, and the maximum difference WD of the temperature of the single battery of the new energy vehicle is:

[0019]

[0020] In the formula, WD is the temperature of the jth single battery of the new energy vehicle, WD is the temperature of the kth single battery of the new energy vehicle, j and k are the numbers of the single batteries, and n is the total number of the single batteries. j k The temperature equalization characteristics of the single battery of the new energy vehicle are calculated:

[0021] The temperature equalization characteristics of the single battery of the new energy vehicle are calculated:

[0022]

[0023] In the formula, Wt is the temperature equalization characteristics of the single battery of the new energy vehicle, υ1 is a set compensation factor of WD, and e is the natural constant.

[0024] The current of each single battery of the new energy vehicle is obtained, and the maximum difference DL of the current of the single battery of the new energy vehicle is:

[0025]

[0026] In the formula, DL is the current of the jth single battery of the new energy vehicle, and DL is the current of the kth single battery of the new energy vehicle. j k The current equalization characteristics of the single battery of the new energy vehicle are calculated:

[0027] The current equalization characteristics of the single battery of the new energy vehicle are calculated:

[0028] ​​

[0029] In the formula, Lt is the new energy vehicle single battery current equalization characteristic, and υ2 is the compensation factor of the set DL;

[0030] The voltage of each single battery of the new energy vehicle is obtained, and the maximum difference DY of the new energy vehicle single battery voltage is:

[0031]

[0032] In the formula, the voltage of the jth single battery of the new energy vehicle is DY j , and the voltage of the kth single battery of the new energy vehicle is DY k .

[0033] The new energy vehicle single battery voltage equalization characteristic Yt is calculated as follows:

[0034]

[0035] In the formula, Yt is the new energy vehicle single battery voltage equalization characteristic, and υ3 is the compensation factor of the set DY;

[0036] Based on the new energy vehicle single battery temperature equalization characteristic, the new energy vehicle single battery current equalization characteristic, and the new energy vehicle single battery voltage equalization characteristic, the new energy vehicle single battery equalization characteristic is obtained through comprehensive analysis, and the new energy vehicle single battery equalization characteristic is used as the analysis basis for the output new energy vehicle battery state.

[0037] As a further scheme, the new energy vehicle single battery equalization characteristic is specifically analyzed as follows:

[0038]

[0039] In the formula, Dtj is the new energy vehicle single battery equalization characteristic.

[0040] As a further scheme, the new energy vehicle battery state is output by combining the new energy vehicle battery pack state factor and the new energy vehicle single battery equalization characteristic and using a neural network model, and the specific analysis process is as follows: based on the trained neural network model, the new energy vehicle battery pack state factor and the new energy vehicle single battery equalization characteristic are input, and the new energy vehicle battery state is output; if the output new energy vehicle battery state is 0, it represents that the new energy vehicle battery state is poor; if the output new energy vehicle battery state is 1, it represents that the new energy vehicle battery state is good; and if the output new energy vehicle battery state is 2, it represents that the new energy vehicle battery state is excellent.

[0041] As a further scheme, the new energy automobile battery usage environment data is acquired, and based on the acquired new energy automobile battery usage environment data, a new energy automobile battery management error term is obtained, and the specific analysis process is as follows: the new energy automobile battery usage environment data is acquired, and the new energy automobile battery usage environment data specifically includes battery usage environment temperature, battery usage environment humidity, and battery usage environment electric field intensity; based on the acquired new energy automobile battery usage environment data, a comprehensive analysis is performed to obtain a new energy automobile battery management error term, and the new energy automobile battery management error term serves as an analysis basis for obtaining a new energy automobile battery management level.

[0042] As a further scheme, the new energy automobile battery management error term is specifically analyzed as follows:

[0043]

[0044] In the formula, λ is the new energy automobile battery management error term, hw is the battery usage environment temperature, hs is the battery usage environment humidity, dcq is the battery usage environment electric field intensity, θ1 is a compensation factor of hw, θ2 is a compensation factor of hs, and θ3 is a compensation factor of dcq.

[0045] As a further scheme, based on the new energy automobile battery state and the new energy automobile battery management error term, a new energy automobile battery management level and a management scheme are obtained, and the specific analysis process is as follows: the new energy automobile battery state and the new energy automobile battery management error term are stored as a specified label, a specified label-new energy automobile battery management level mapping table pre-stored in a database is acquired, a matching new energy automobile battery management level is found according to the specified label by searching the mapping table; a new energy automobile battery management level-new energy automobile battery management scheme mapping table pre-stored in the database is acquired, and a matching new energy automobile battery management scheme is found according to the new energy automobile battery management level by searching the mapping table.

[0046] The second aspect of the application provides a new energy automobile battery management method, including the following steps: analyzing a new energy automobile battery pack state, obtaining a new energy automobile battery pack state factor based on the analysis result of the new energy automobile battery pack state; analyzing a new energy automobile single battery equalization state to obtain a new energy automobile single battery equalization characteristic; combining the new energy automobile battery pack state factor, the new energy automobile single battery equalization characteristic, and using a neural network model to output a new energy automobile battery state; acquiring new energy automobile battery usage environment data, obtaining a new energy automobile battery management error term based on the acquired new energy automobile battery usage environment data; and based on the new energy automobile battery state and the new energy automobile battery management error term, obtaining a new energy automobile battery management level and a management scheme.

[0047] Compared with the prior art, the embodiments of the present application have at least the following advantages or beneficial effects:

[0048] (1) The present application provides a new energy vehicle battery management system and method. The battery pack state analysis module focuses on the entire battery pack, considering its overall performance indicators. The single cell equalization state analysis module goes deep into each single cell battery, analyzes the equalization between single cells, and obtains the single cell equalization characteristics, avoiding the evaluation deviation caused by only focusing on the whole or single cell.

[0049] (2) The battery state output module of the present application uses a neural network model to accurately output the state of the battery according to different battery pack states and single cell equalization conditions. The battery management error term analysis module is used to obtain battery usage environment data, so that the battery management system can more accurately understand the real state of the battery. After considering the error caused by environmental factors, the battery management update module can determine the battery management level and management scheme according to the battery state and management error term.

[0050] (3) The present application obtains the new energy vehicle battery management level and management scheme through the new energy vehicle battery state and new energy vehicle battery management error term, which can take the most appropriate management measures for batteries in different states. The battery management error term takes into account the influence of factors such as battery usage environment on the accuracy of the battery management system. Different environmental conditions will cause errors in the battery management system when monitoring and controlling the battery. By considering these error terms, the management scheme can be adjusted more accurately. Precise management level division and scheme implementation help to reduce the loss of batteries during use, optimize the performance of new energy vehicle batteries, and improve system reliability and safety. BRIEF DESCRIPTION OF DRAWINGS

[0051] The present application will be further described with the help of the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application. For ordinary skilled in the art, other drawings can be obtained without creative labor on the basis of the following drawings.

[0052] Figure 1 The system module connection diagram of the present application is shown in the figure.

[0053] Figure 2 The method step flowchart of the present application is shown in the figure. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described in detail below with the help of the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0055] Referring to Figure 1 The first aspect of the present application provides a new energy vehicle battery management system, comprising a battery pack state analysis module, a single cell equalization state analysis module, a battery state output module, a battery management error term analysis module and a battery management update module.

[0056] The battery pack state analysis module is used for analyzing the state of the new energy vehicle battery pack, and based on the analysis result of the state of the new energy vehicle battery pack, a new energy vehicle battery pack state factor is obtained.

[0057] The specific analysis process is: considering the capacity loss of the battery pack under different charge and discharge rates, a rate correction coefficient is introduced to obtain an improved ampere-hour integral formula:

[0058]

[0059] In the formula, SOC is the state of charge of the battery pack, SOC0 is the initial state of charge of the battery pack, C n is the rated capacity of the battery pack, I b (t) is the charge and discharge current of the battery pack at time t, K r is the rate correction coefficient stored in the database;

[0060] The health status of the battery pack is calculated:

[0061]

[0062] In the formula, SOH is the health status of the battery pack, C a is the current actual available capacity of the battery pack, and A is the calendar aging factor stored in the database;

[0063] The relationship between the growth of the internal resistance of the battery pack and the temperature is described in combination with the Arrhenius equation, and a battery pack internal resistance growth model is constructed:

[0064]

[0065] In the formula, R i is the internal resistance of the battery pack, R i0 is the initial internal resistance of the battery pack, β and γ are fitting parameters stored in the database, E a is the activation energy, R is the gas constant, T is the absolute temperature of the battery pack, E is the cumulative charge and discharge energy of the battery pack, and e is the natural constant.

[0066] The electrochemical reaction occurring inside the battery needs the reactant molecules to overcome a certain energy barrier to proceed, and this energy barrier is the activation energy. With the change of the internal conditions during the use of the battery, the activation energy will affect the size of the internal resistance of the battery.

[0067] The physical and chemical processes inside the battery are similar to the processes related to molecular motion and energy relationships described by the ideal gas state equation. The gas constant stored in the database helps quantify the relationship between the internal resistance of the battery and the temperature.

[0068] Based on the analysis results of the state of the new energy vehicle battery pack, the state factor of the new energy vehicle battery pack is obtained, and the specific analysis process is: based on the state of charge of the battery pack, the health status of the battery pack and the internal resistance of the battery pack, the state factor of the new energy vehicle battery pack is obtained by comprehensive analysis, and the state factor of the new energy vehicle battery pack is used as the basis for analyzing the output state of the new energy vehicle battery;

[0069]

[0070] In the formula, Dcz is the state factor of the new energy vehicle battery pack.

[0071] In practical applications, the charging and discharging current of the battery is constantly changing, and this modified state of charge evaluation can more accurately reflect the actual state of charge of the battery under complex working conditions. For example, in the case of frequent acceleration and deceleration of the vehicle, which leads to frequent changes in current, the problem of inaccurate SOC estimation caused by cumulative error of traditional ampere-hour integration method can be avoided.

[0072] The health status (SOH) evaluation not only focuses on the capacity degradation of the battery, but also considers the influence of time factor on the aging of the battery. This helps to better understand the health status of the battery, because the battery will also decrease in performance due to the passage of time even without charging and discharging operation during long-term storage or use. The internal resistance of the battery is an important factor affecting the performance and life of the battery.

[0073] Through the constructed internal resistance growth model of the battery pack, the change of internal resistance can be accurately predicted according to the working temperature of the battery. For example, in high temperature environment, the internal resistance of the battery may increase, leading to increased energy loss during charging and discharging process, so that appropriate measures can be taken in advance, such as adjusting the charging strategy or starting the thermal management system.

[0074] By combining SOC, SOH and internal resistance to obtain the state factor of the battery pack, a comprehensive index is provided for evaluating the state of the new energy vehicle battery. It can be used as the basis for analyzing the output state of the new energy vehicle battery, helping the battery management system to better understand the overall performance of the battery, and then taking more reasonable management strategies, such as optimizing charging and discharging control, and deciding whether to perform battery balancing operation, etc.

[0075] The single cell equalization state analysis module is used to analyze the equalization state of the single cell of the new energy vehicle, and obtain the equalization characteristics of the single cell of the new energy vehicle.

[0076] The specific analysis process is: obtaining the temperature of each single battery of the new energy vehicle, the maximum difference WD of the single battery temperature of the new energy vehicle is:

[0077]

[0078] In the formula, the temperature of the jth single battery of the new energy vehicle is WD j , the temperature of the kth single battery of the new energy vehicle is WD k , j and k are the numbers of the single batteries, and n is the total number of the single batteries;

[0079] The temperature equalization feature of the single battery of the new energy vehicle is calculated:

[0080]

[0081] In the formula, Wt is the temperature equalization feature of the single battery of the new energy vehicle, and υ1 is a compensation factor of WD set;

[0082] Obtaining the current of each single battery of the new energy vehicle, the maximum difference DL of the single battery current of the new energy vehicle is:

[0083]

[0084] In the formula, the current of the jth single battery of the new energy vehicle is DL j , and the current of the kth single battery of the new energy vehicle is DL k ;

[0085] The current equalization feature of the single battery of the new energy vehicle is calculated:

[0086]

[0087] In the formula, Lt is the current equalization feature of the single battery of the new energy vehicle, and υ2 is a compensation factor of DL set;

[0088] Obtaining the voltage of each single battery of the new energy vehicle, the maximum difference DY of the single battery voltage of the new energy vehicle is:

[0089]

[0090] In the formula, the voltage of the jth single battery of the new energy vehicle is DY j , and the voltage of the kth single battery of the new energy vehicle is DY k ;

[0091] The voltage equalization feature of the single battery of the new energy vehicle is calculated:

[0092]

[0093] In the formula, Yt is the voltage equalization characteristic of the single battery of the new energy vehicle, and υ3 is the compensation factor of the set DY.

[0094] Based on the temperature equalization characteristic, the current equalization characteristic, and the voltage equalization characteristic of the single battery of the new energy vehicle, the equalization characteristic of the single battery of the new energy vehicle is obtained through comprehensive analysis, and the equalization characteristic of the single battery of the new energy vehicle is used as the analysis basis for outputting the state of the new energy vehicle battery.

[0095] The equalization characteristic of the single battery of the new energy vehicle is specifically analyzed as follows:

[0096]

[0097] In the formula, Dtj is the equalization characteristic of the single battery of the new energy vehicle.

[0098] The maximum temperature difference of the single battery is calculated, which can directly reflect the dispersion degree of the temperature of the single battery in the battery pack. In actual application, the uneven temperature of the single battery in the battery pack will affect the overall performance and service life of the battery pack. The temperature equalization characteristic of the single battery is calculated, which comprehensively considers the temperature difference and the compensation factor, and can more accurately quantify the temperature equalization of the single battery, providing a basis for subsequent battery management.

[0099] The maximum current difference of the single battery is calculated. During the charging and discharging process of the battery pack, the unbalanced current of the single battery will cause some batteries to be overcharged or overdischarged, affecting the service life of the battery. The current equalization characteristic of the single battery can comprehensively consider the current difference and the compensation factor, and more accurately reflect the current equalization state of the single battery.

[0100] The unbalanced voltage of the single battery is one of the common problems of the battery pack, and the unbalanced voltage will cause the overall performance of the battery pack to decline. After long-term use, some batteries may have a large voltage difference due to different aging degrees, and this voltage imbalance phenomenon can be discovered in time through monitoring.

[0101] The temperature, current, and voltage equalization characteristics of the single battery are comprehensively considered to obtain the equalization characteristic of the single battery, which can be used as an important basis for judging the state of the new energy vehicle battery. Through analysis of the equalization characteristic of the single battery, the battery management system can more accurately understand the working state of each single battery in the battery pack, and then take effective equalization strategies such as active equalization or passive equalization to ensure the efficient and stable operation of the battery pack and prolong the service life of the battery pack.

[0102] The battery state output module is used to combine the new energy vehicle battery pack state factor and the equalization characteristic of the single battery of the new energy vehicle, and use a neural network model to output the state of the new energy vehicle battery.

[0103] The specific analysis process is: based on the trained neural network model, input the new energy vehicle battery pack state factor and the new energy vehicle single battery equalization characteristics (the input layer has two neurons), output the new energy vehicle battery state; if the output new energy vehicle battery state is 0, it represents that the new energy vehicle battery state is bad; if the output new energy vehicle battery state is 1, it represents that the new energy vehicle battery state is good; if the output new energy vehicle battery state is 2, it represents that the new energy vehicle battery state is excellent.

[0104] For the training of the neural network model:

[0105] Collect a large amount of new energy vehicle battery related data, including battery pack state factors (covering state of charge SOC, state of health SOH, internal resistance under different working conditions), single battery equalization characteristics (temperature, current, voltage equalization related data) and corresponding battery actual state (bad, good, excellent). At the same time, collect battery use environment data (temperature, humidity, electric field intensity) for subsequent analysis of its influence on the model.

[0106] Clean the collected data, remove outliers and error data. For example, check if the SOC and SOH values are within a reasonable range, and if there are obvious measurement errors in the current and voltage data.

[0107] Normalize the data, map different dimensional data to the same scale, for example, normalize the SOC and SOH values in the battery pack state factor to the [0, 1] interval, which helps to improve the efficiency and stability of model training.

[0108] According to the characteristics of the problem and the nature of the data, select the appropriate neural network structure, such as multilayer perceptron (MLP). Determine the number of input layer neurons, such as the battery pack state factor and single battery equalization characteristics as input, which may be set according to the number of specific indicators, such as SOC, SOH, internal resistance, single battery temperature equalization characteristics, current equalization characteristics, voltage equalization characteristics, etc. may correspond to different input neurons.

[0109] Determine the number of hidden layers and the number of neurons in each layer, which can be determined by testing different structure combinations and using the validation set to evaluate the performance of the model. Generally, start with a simple structure, such as 1-2 hidden layers, and the number of neurons in each layer is between the number of input neurons and the number of output neurons.

[0110] Determine the number of output layer neurons as 1, because the final output is the battery state (0, 1, 2), which can be converted into the probability distribution of the corresponding category through a suitable activation function (such as softmax function).

[0111] A suitable loss function, such as cross-entropy loss, is chosen to measure the difference between the model's output and the true labels (battery status). Cross-entropy loss is well-suited for multi-classification problems and is appropriate for the classification of battery status in this case.

[0112] An optimizer, such as stochastic gradient descent (SGD), Adagrad, Adadelta, Adam, etc., is determined, as these optimizers have different advantages in different scenarios. The Adam optimizer generally performs well in practice, as it adaptively adjusts the learning rate and can converge faster during training. Set a suitable learning rate, with an initial learning rate between 0.001 and 0.1, and adjust it based on the loss changes during training. If the loss decreases too slowly, increase the learning rate, and if there is oscillation or non-convergence, decrease the learning rate.

[0113] Set the number of training epochs, which can be initially set to a larger value, such as 100-1000 epochs. However, during training, observe the loss and accuracy of the validation set, and stop training early when the model's performance on the validation set no longer improves, to prevent overfitting. Also, set an appropriate batch size, such as 32, 64, 128, etc. A larger batch size can take advantage of matrix operations to improve training speed, but may consume more memory, so it needs to be selected based on hardware resources and data characteristics.

[0114] Divide the preprocessed data into training, validation, and test sets, usually in a 70%, 15%, 15% ratio. During training, take data from the training set in batches and input it into the neural network, calculate the output through forward propagation, then calculate the loss based on the loss function, and update the neural network parameters through the optimizer.

[0115] After training for a certain number of epochs (e.g., 10 epochs), evaluate the model's performance on the validation set, calculate accuracy, recall, F1 value, etc., and observe whether the model has overfitting or underfitting. If the loss on the validation set continues to rise or the accuracy no longer improves, you may need to adjust the model structure, training parameters, or increase the amount of data.

[0116] After training is complete, use the test set to evaluate the model's final performance and obtain its performance indicators in actual application. If the model's performance does not meet expectations, further analyze the reasons, such as data quality, model structure complexity, training parameter settings, etc., and optimize and improve the model, such as adjusting the number of hidden layer neurons, changing the activation function, optimizing data preprocessing methods, etc., then retrain and evaluate the model until its performance meets the requirements.

[0117] The neural network model takes battery pack state factors and single battery equalization features as inputs. The battery pack state factors cover the performance indicators of the battery pack as a whole, and the single battery equalization features reflect the performance differences between each single battery in the battery pack, including voltage equalization, current equalization, and temperature equalization. Considering these two key factors comprehensively can fully depict the actual condition of the battery and avoid the one-sidedness brought by relying on a single factor to evaluate the battery state.

[0118] The neural network has strong non-linear fitting capability. The relationship between the battery state and the battery pack state factors and the single battery equalization features is often non-linear, and the neural network can fit this complex non-linear relationship by learning a large amount of data, so as to accurately judge the battery state, whether in normal working conditions or in extreme conditions, and give a more reliable evaluation result.

[0119] The battery state is divided into three levels: poor (0), good (1), and excellent (2). This classification method is simple and intuitive, and is convenient for users and vehicle management systems to understand and operate.

[0120] The battery management error term analysis module is used to obtain new energy vehicle battery use environment data, and based on the obtained new energy vehicle battery use environment data, the new energy vehicle battery management error term is obtained.

[0121] The specific analysis process is: obtaining new energy vehicle battery use environment data, the new energy vehicle battery use environment data specifically includes battery use environment temperature, battery use environment humidity, and battery use environment electric field intensity; based on the obtained new energy vehicle battery use environment data, the new energy vehicle battery management error term is obtained by comprehensive analysis, and the new energy vehicle battery management error term is used as the basis for analyzing the new energy vehicle battery management level.

[0122] The new energy vehicle battery management error term is specifically analyzed as follows:

[0123]

[0124] In the formula, λ is the new energy vehicle battery management error term, hw is the battery use environment temperature, hs is the battery use environment humidity, dcq is the battery use environment electric field intensity, θ1 is the compensation factor of hw, θ2 is the compensation factor of hs, and θ3 is the compensation factor of dcq.

[0125] By obtaining new energy vehicle battery usage environment data, including battery usage environment temperature, humidity and electric field intensity and other factors, the battery management error term is calculated. These environmental factors have an important influence on the performance of the battery and the accuracy of the battery management system. Temperature can affect the chemical reaction rate of the battery, and thus affect the charging and discharging performance of the battery; humidity may affect the insulation performance of the battery; electric field intensity may interfere with the electronic components of the battery management system.

[0126] The battery management error term can be used as a basis for analyzing the new energy vehicle battery management level. Accurate error term analysis helps to develop a reasonable battery management strategy. Under different environmental conditions, the performance and management needs of the battery are different. Through the analysis of the battery management error term, the battery management system can dynamically adjust the management strategy according to the current environmental conditions. Precise battery management error term analysis helps to prevent excessive degradation of battery performance and ensure battery safety.

[0127] The battery management updating module is used to obtain the new energy vehicle battery management level and management scheme based on the new energy vehicle battery state and the new energy vehicle battery management error term.

[0128] The specific analysis process is as follows: store the new energy vehicle battery state and the new energy vehicle battery management error term as a specified label, obtain the pre-stored specified label-new energy vehicle battery management level mapping table in the database, find the matching new energy vehicle battery management level according to the specified label by looking up the mapping table; obtain the pre-stored new energy vehicle battery management level-new energy vehicle battery management scheme mapping table in the database, and find the matching new energy vehicle battery management scheme according to the new energy vehicle battery management level by looking up the mapping table.

[0129] By storing the battery state and the battery management error term as a specified label, and determining the management level and scheme according to the pre-stored mapping table, the standardization of new energy vehicle battery management is realized. This standardization helps to adopt a unified management specification among different vehicles and different battery systems, avoiding the confusion caused by inconsistent management methods. The pre-stored mapping table is constructed based on a large amount of experimental data and actual operation experience, and can accurately match the battery state, management error term and appropriate management level and scheme. This avoids the subjectivity and uncertainty that may occur in human judgment, ensures that the battery always operates under the best management strategy, and thus improves the performance and service life of the battery.

[0130] By accurately determining the management level and scheme according to the battery state and the management error term, the safety of the battery can be better guaranteed. When the battery management error term is large, it indicates that the battery may be affected by adverse environmental factors, and the corresponding management scheme can be found from the mapping table in time, such as increasing the monitoring frequency, reducing the charging rate or strengthening the temperature monitoring, and replacing the battery if necessary, to prevent the battery from overheating, overcharging, damage and other safety hazards.

[0131] Referring to Figure 2 The second aspect of the present application provides a new energy vehicle battery management method, comprising the following steps: analyzing the state of the new energy vehicle battery pack, and obtaining the state factor of the new energy vehicle battery pack based on the analysis result of the state of the new energy vehicle battery pack.

[0132] The state of the new energy vehicle single battery is analyzed to obtain the equalization characteristics of the new energy vehicle single battery.

[0133] The state of the new energy vehicle battery is output by combining the state factor of the new energy vehicle battery pack, the equalization characteristics of the new energy vehicle single battery, and using a neural network model.

[0134] The use environment data of the new energy vehicle battery is obtained, and the management error term of the new energy vehicle battery is obtained based on the obtained use environment data of the new energy vehicle battery.

[0135] The management level and scheme of the new energy vehicle battery are obtained based on the state of the new energy vehicle battery and the management error term of the new energy vehicle battery.

[0136] The above content is only an example and description of the structure of the present application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the structure of the present application or exceed the scope defined by the present claims, and should belong to the protection scope of the present application.

Claims

1. A battery management system for new energy vehicles, characterized in that, It includes a battery pack status analysis module, a single-cell equalization status analysis module, a battery status output module, a battery management error term analysis module, and a battery management update module, among which: The battery pack state analysis module is used to analyze the state of the new energy vehicle battery pack and obtain the new energy vehicle battery pack state factor based on the analysis results. The single-cell equilibrium state analysis module is used to analyze the equilibrium state of single-cell batteries in new energy vehicles and obtain the equilibrium characteristics of single-cell batteries in new energy vehicles. The battery status output module is used to combine the battery pack status factor of the new energy vehicle and the equalization characteristics of the individual battery cells of the new energy vehicle, and use a neural network model to output the battery status of the new energy vehicle. The battery management error term analysis module is used to acquire new energy vehicle battery usage environment data and obtain new energy vehicle battery management error terms based on the acquired new energy vehicle battery usage environment data. The battery management update module is used to obtain the battery management level and management scheme of the new energy vehicle based on the battery status and battery management error items of the new energy vehicle. The analysis of the battery pack status of new energy vehicles is specifically carried out as follows: Considering the capacity loss of the battery pack at different charge and discharge rates, a rate correction factor is introduced to obtain an improved ampere-hour integral formula: ； In the formula, The state of charge of the battery pack. This represents the initial state of charge of the battery pack. The rated capacity of the battery pack. Let be the battery pack charging and discharging current at time t. The multiplier correction factor is stored in the database; Calculate the health status of the battery pack: ； In the formula, For the health status of the battery pack, This represents the current actual usable capacity of the battery pack. The calendar aging factor stored in the database; By combining the Arrhenius equation to describe the relationship between the increase in internal resistance of the battery pack and temperature, a model for the increase in internal resistance of the battery pack is constructed: ； In the formula, This refers to the internal resistance of the battery pack. This represents the initial internal resistance of the battery pack. , These are the fitting parameters stored in the database. For activation energy, The gas constant is... Here, E is the absolute temperature of the battery pack, E is the cumulative charge and discharge energy of the battery pack, and e is the natural constant. The state factor of the new energy vehicle battery pack is obtained based on the state analysis results of the new energy vehicle battery pack. The specific analysis process is as follows: Based on the state of charge, health status, and internal resistance of the battery pack, a comprehensive analysis is conducted to obtain the state factor of the new energy vehicle battery pack. The state factor of the new energy vehicle battery pack serves as the basis for analyzing and outputting the state of the new energy vehicle battery. ； In the formula, State factor for new energy vehicle battery packs; The analysis of the equilibrium state of individual batteries in new energy vehicles yields the equilibrium characteristics of individual batteries in new energy vehicles. The specific analysis process is as follows: Obtain the temperature of each individual battery cell in a new energy vehicle, and the maximum temperature difference between individual battery cells in a new energy vehicle. for: ； In the formula, the temperature of the j-th individual battery cell in the new energy vehicle is... The temperature of the kth individual battery cell in a new energy vehicle is j and k are the individual cell numbers, and n is the total number of individual cells; Calculate the temperature uniformity characteristics of individual battery cells in new energy vehicles: ； In the formula, To ensure the temperature uniformity of individual battery cells in new energy vehicles. For setting The compensation factor, e is the natural constant; Obtain the current of each individual battery cell in a new energy vehicle, and the maximum difference in current between individual battery cells in a new energy vehicle. for: ； In the formula, the current of the j-th individual battery cell in the new energy vehicle is The current of the kth single-cell battery in a new energy vehicle is ; Calculate the current balance characteristics of individual battery cells in new energy vehicles: ； In the formula, This refers to the current balancing characteristics of individual battery cells in new energy vehicles. For setting Compensation factor; Obtain the voltage of each individual battery cell in a new energy vehicle, and the maximum voltage difference between individual battery cells in a new energy vehicle. for: ； In the formula, the voltage of the j-th individual battery cell in the new energy vehicle is The voltage of the kth individual battery cell in a new energy vehicle is ; Calculate the voltage balance characteristics of individual battery cells in new energy vehicles: ； In the formula, This is to address the voltage balance characteristics of individual battery cells in new energy vehicles. For setting Compensation factor; Based on the temperature balance characteristics, current balance characteristics, and voltage balance characteristics of individual batteries in new energy vehicles, a comprehensive analysis is conducted to obtain the balance characteristics of individual batteries in new energy vehicles. These balance characteristics serve as the basis for analyzing the output state of new energy vehicle batteries.

2. The new energy vehicle battery management system according to claim 1, characterized in that: The specific analysis process for the balanced characteristics of individual batteries in new energy vehicles is as follows: ； In the formula, This refers to the balanced characteristics of individual batteries in new energy vehicles.

3. The new energy vehicle battery management system according to claim 1, characterized in that: The analysis process involves combining the state factors of new energy vehicle battery packs and the equalization characteristics of individual new energy vehicle cells, and using a neural network model to output the state of new energy vehicle batteries. Based on the trained neural network model, the state factors of the new energy vehicle battery pack and the balance characteristics of the new energy vehicle individual battery are input, and the state of the new energy vehicle battery is output. If the output of the new energy vehicle battery status is 0, it means that the new energy vehicle battery status is bad. If the output of the new energy vehicle battery status is 1, it means that the new energy vehicle battery status is good. If the output of the new energy vehicle battery status is 2, it means that the new energy vehicle battery status is excellent.

4. A new energy vehicle battery management system according to claim 1, characterized in that: The process of acquiring environmental data on the use of new energy vehicle batteries, and then obtaining the new energy vehicle battery management error term based on this data, is as follows: Acquire data on the operating environment of new energy vehicle batteries. Specifically, this data includes the ambient temperature, humidity, and electric field strength of the battery operating environment. Based on the acquired environmental data of new energy vehicle batteries, a comprehensive analysis is conducted to obtain the new energy vehicle battery management error item, which serves as the basis for determining the new energy vehicle battery management level.

5. A new energy vehicle battery management system according to claim 4, characterized in that: The specific analysis process for the new energy vehicle battery management error item is as follows: ； In the formula, This is an error item for battery management in new energy vehicles. For battery operating temperature, Humidity of the battery operating environment, The electric field strength of the battery's operating environment. For setting Compensation factor, For setting Compensation factor, For setting The compensation factor.

6. A new energy vehicle battery management system according to claim 1, characterized in that: The battery management level and management scheme for new energy vehicles are obtained based on the battery status and battery management error items. The specific analysis process is as follows: The battery status and battery management error items of new energy vehicles are stored as specified tags. The specified tag-new energy vehicle battery management level mapping table is obtained from the database. By searching the mapping table, the matching new energy vehicle battery management level is found according to the specified tag. Retrieve the pre-stored mapping table of new energy vehicle battery management level and new energy vehicle battery management solution in the database, and find the matching new energy vehicle battery management solution based on the new energy vehicle battery management level by searching the mapping table.

7. A method for managing a new energy vehicle battery, applied to a new energy vehicle battery management system as described in any one of claims 1-6, characterized in that, Includes the following steps: The state of the battery pack in new energy vehicles is analyzed, and the state factor of the battery pack in new energy vehicles is obtained based on the analysis results. The equilibrium state of individual batteries in new energy vehicles is analyzed to obtain the equilibrium characteristics of individual batteries in new energy vehicles. By combining the state factors of new energy vehicle battery packs and the balance characteristics of individual cells in new energy vehicles, and using a neural network model, the state of new energy vehicle batteries is output. Obtain environmental data on the use of new energy vehicle batteries, and based on the obtained environmental data, obtain the new energy vehicle battery management error term; Based on the battery status and battery management error terms of new energy vehicles, the battery management level and management scheme of new energy vehicles are obtained.

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